The answer is a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling. This configuration is the most cost-effective because MMEs allow multiple models to share a single endpoint and underlying instance, dramatically reducing infrastructure costs while the ml.c5.4xlarge’s 16 vCPUs and 32 GB memory provide ample compute for XGBoost’s low-latency inference, easily meeting the sub-10 ms and 1,000 requests per second requirements. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to balance cost and performance for real-time inference, often trapping candidates who default to a single-model endpoint or over-provision with GPU instances. Remember that for tree-based models like XGBoost, CPU instances are typically sufficient and more cost-effective than GPUs. Memory tip: “MME for MLE” — Multi-Model Endpoints are the go-to for cost-effective, real-time inference on CPU-friendly models.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
Option B is correct because a Multi-Model Endpoint (MME) on a single ml.c5.4xlarge instance allows multiple models to share the same endpoint, reducing cost while still meeting the latency (<10 ms) and throughput (1000 req/s) requirements. The ml.c5.4xlarge provides sufficient compute (16 vCPUs, 32 GB memory) for XGBoost inference, and auto scaling ensures capacity adjusts to handle peak load without over-provisioning.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use SageMaker Batch Transform with multiple instances
Why it's wrong here
Batch Transform is for offline inference, not real-time requests.
✓
Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling
Why this is correct
MME allows multiple models to share a container, reducing cost while scaling to meet demand.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy on a single ml.c5.xlarge instance with a real-time endpoint
Why it's wrong here
A single instance may not handle 1000 req/s with low latency; also cost may be higher if overprovisioned.
✗
Deploy separate real-time endpoints for each model on ml.m5.large instances
Why it's wrong here
Separate endpoints increase cost and management overhead without performance benefit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a single large instance is insufficient for high throughput, but MME allows efficient resource sharing across models, making a single ml.c5.4xlarge cost-effective when the model is small and CPU-bound.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker MME uses a shared inference container that loads models from Amazon S3 on demand, caching them in memory to reduce cold-start latency. For XGBoost, the model size is typically small (a few MB), so multiple models can coexist on one instance without significant memory contention. In real-world scenarios, auto scaling policies should be based on the 'InvocationsPerInstance' CloudWatch metric to ensure the endpoint scales before latency degrades beyond 10 ms.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling — Option B is correct because a Multi-Model Endpoint (MME) on a single ml.c5.4xlarge instance allows multiple models to share the same endpoint, reducing cost while still meeting the latency (<10 ms) and throughput (1000 req/s) requirements. The ml.c5.4xlarge provides sufficient compute (16 vCPUs, 32 GB memory) for XGBoost inference, and auto scaling ensures capacity adjusts to handle peak load without over-provisioning.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A trained model needs to be deployed for real-time inference with low latency. Which AWS service is best suited for this?
medium
A.SageMaker Batch Transform
✓ B.SageMaker endpoints
C.SageMaker Hyperparameter Tuning
D.AWS Lambda with model packaged
Why B: SageMaker endpoints provide managed, scalable, and low-latency real-time inference. Batch Transform is for offline inference, Hyperparameter Tuning is for training, and Lambda is for serverless but lacks native ML optimizations.
Variation 2. A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?
hard
A.Use a multi-model endpoint
B.Add Elastic Inference
C.Enable SageMaker Batch Transform
✓ D.Switch to a larger instance type
Why D: Option B is correct because switching to a larger instance type provides more compute capacity, reducing inference latency. Option A is wrong because multi-model endpoints may increase latency due to model loading. C is wrong because Batch Transform is for batch, not real-time. D is wrong because Elastic Inference adds GPU acceleration but may not reduce latency as much as compute upgrade.
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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
Question Discussion
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